Nitesh V. Chawla

research

∙ 09/02/2023

Pure Message Passing Can Estimate Common Neighbor for Link Prediction

Message Passing Neural Networks (MPNNs) have emerged as the de facto sta...

0 Kaiwen Dong, et al. ∙

research

∙ 05/31/2023

Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health

This tutorial paper provides a general overview of symbolic regression (...

0 Jennifer J. Schnur, et al. ∙

research

∙ 05/27/2023

What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks

Large Language Models (LLMs) with strong abilities in natural language p...

0 Taicheng Guo, et al. ∙

research

∙ 04/12/2023

Towards Understanding How Data Augmentation Works with Imbalanced Data

Data augmentation forms the cornerstone of many modern machine learning ...

0 Damien A. Dablain, et al. ∙

research

∙ 04/09/2023

Class-Imbalanced Learning on Graphs: A Survey

The rapid advancement in data-driven research has increased the demand f...

0 Yihong Ma, et al. ∙

research

∙ 02/01/2023

Knowledge Distillation on Graphs: A Survey

Graph Neural Networks (GNNs) have attracted tremendous attention by demo...

0 Yijun Tian, et al. ∙

research

∙ 12/15/2022

Interpretable ML for Imbalanced Data

Deep learning models are being increasingly applied to imbalanced data i...

0 Damien A. Dablain, et al. ∙

research

∙ 11/29/2022

FakeEdge: Alleviate Dataset Shift in Link Prediction

Link prediction is a crucial problem in graph-structured data. Due to th...

0 Kaiwen Dong, et al. ∙

research

∙ 08/22/2022

NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs

While Graph Neural Networks (GNNs) have demonstrated their efficacy in d...

0 Yijun Tian, et al. ∙

research

∙ 08/21/2022

Heterogeneous Graph Masked Autoencoders

Generative self-supervised learning (SSL), especially masked autoencoder...

0 Yijun Tian, et al. ∙

research

∙ 07/08/2022

Graph-based Molecular Representation Learning

Molecular representation learning (MRL) is a key step to build the conne...

0 Zhichun Guo, et al. ∙

research

∙ 05/27/2022

Deep Ensembles for Graphs with Higher-order Dependencies

Graph neural networks (GNNs) continue to achieve state-of-the-art perfor...

0 Steven J. Krieg, et al. ∙

research

∙ 05/24/2022

Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks

Learning effective recipe representations is essential in food studies. ...

0 Yijun Tian, et al. ∙

research

∙ 05/24/2022

RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation

Recipe recommendation systems play an essential role in helping people d...

0 Yijun Tian, et al. ∙

research

∙ 03/19/2022

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance

Comprehensively evaluating and comparing researchers' academic performan...

0 Zhichun Guo, et al. ∙

research

∙ 01/12/2022

Predicting Terrorist Attacks in the United States using Localized News Data

Terrorism is a major problem worldwide, causing thousands of fatalities ...

0 Steven J. Krieg, et al. ∙

research

∙ 06/04/2021

Graph Barlow Twins: A self-supervised representation learning framework for graphs

The self-supervised learning (SSL) paradigm is an essential exploration ...

25 Piotr Bielak, et al. ∙

research

∙ 05/05/2021

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Despite over two decades of progress, imbalanced data is still considere...

99 Damien Dablain, et al. ∙

research

∙ 02/16/2021

Few-Shot Graph Learning for Molecular Property Prediction

The recent success of graph neural networks has significantly boosted mo...

0 Zhichun Guo, et al. ∙

research

∙ 12/29/2020

AttrE2vec: Unsupervised Attributed Edge Representation Learning

Representation learning has overcome the often arduous and manual featur...

13 Piotr Bielak, et al. ∙

research

∙ 07/25/2020

Learning Attribute-Structure Co-Evolutions in Dynamic Graphs

Most graph neural network models learn embeddings of nodes in static att...

0 Daheng Wang, et al. ∙

research

∙ 06/17/2020

Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network

Noun phrases and relational phrases in Open Knowledge Bases are often no...

0 Tianwen Jiang, et al. ∙

research

∙ 06/11/2020

Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

User behavior modeling is important for industrial applications such as ...

0 Daheng Wang, et al. ∙

research

∙ 06/10/2020

Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic

We present and begin to explore a collection of social data that represe...

0 Steven J. Krieg, et al. ∙

research

∙ 06/10/2020

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being

Assessment of job performance, personalized health and psychometric meas...

8 Pablo Robles-Granda, et al. ∙

research

∙ 11/26/2019

Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs) serve as useful resources for various natural lan...

0 Chuxu Zhang, et al. ∙

research

∙ 10/07/2019

Graph Few-shot Learning via Knowledge Transfer

Towards the challenging problem of semi-supervised node classification, ...

20 Huaxiu Yao, et al. ∙

research

∙ 08/15/2019

HONEM: Network Embedding Using Higher-Order Patterns in Sequential Data

Representation learning offers a powerful alternative to the oft painsta...

5 Mandana Saebi, et al. ∙

research

∙ 06/26/2019

Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements

Conditions are essential in the statements of biological literature. Wit...

0 Tianwen Jiang, et al. ∙

research

∙ 04/06/2019

Incremental embedding for temporal networks

Prediction over edges and nodes in graphs requires appropriate and effic...

0 Tomasz Kajdanowicz, et al. ∙

research

∙ 11/20/2018

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

Nowadays, multivariate time series data are increasingly collected in va...

0 Chuxu Zhang, et al. ∙

research

∙ 04/11/2018

Long-term Compliance Habits: What Early Data Tells Us

The rise in popularity of physical activity trackers provides extensive ...

0 Louis Faust, et al. ∙

research

∙ 06/01/2017

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally ope...

0 Keith Feldman, et al. ∙

research

∙ 02/02/2017

HoNVis: Visualizing and Exploring Higher-Order Networks

Unlike the conventional first-order network (FoN), the higher-order netw...

0 Jun Tao, et al. ∙

research

∙ 06/19/2016

Can Scientific Impact Be Predicted?

A widely used measure of scientific impact is citations. However, due to...

0 Yuxiao Dong, et al. ∙

research

∙ 12/15/2014

Will This Paper Increase Your h-index? Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output o...

0 Yuxiao Dong, et al. ∙

research

∙ 02/09/2011

Predictors of short-term decay of cell phone contacts in a large scale communication network

Under what conditions is an edge present in a social network at time t l...

0 Troy Raeder, et al. ∙

Nitesh V. Chawla

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